Nick Jordan|
11.09.18 |CommunityInsights

Making the Business Case for Machine Learning Part 1

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On October 24, the Next Matters Most MeetUp held a panel discussion on Machine Learning. The event brought Machine Learning experts together with members of the Triangle Community who (like me) wanted to learn more.

While people know Machine Learning is out there and can explain the tech value of it, the current challenge is how to demonstrate the business value. The panelists shared different ways of doing this effectively – and we even got some great ROI stats courtesy of Richard Boyd.

The conversation was non-stop throughout the evening with lots of “war stories” and cautionary tales shared. It was great to see people arrive early and stay for the 30+ minutes of Q&A after the panel ended.

For those who couldn’t attend, we will be sharing a comprehensive recap of the evening over a few posts. If you’d like to watch the panel yourself, we live streamed the panel on Periscope and the recording is available on our Twitter feed.

Quick Intro to the Panelists

The panel featured five industry experts who have been using Machine Learning to bring value to their organizations for years.

Making the Business Case

Ken got the evening started by asking the panel to share what value business owners should anticipate from Machine Learning.

Richard believes the business case for Machine Learning is starting to take shape. Companies he has worked with are seeing 10x ROI on projects taking six months to implement and costing around $200k (or somewhere in the six figure range). He even mentioned having several Fortune 100 companies get 1500x ROI.

At the end of the day, Machine Learning brings measurable ROI plus annuity and this makes it easy to sell the value up the chain.

Brooks took a more philosophical approach pointing out two main business drivers for Machine Learning: scalability and forecasting.

Machine Learning enables teams to scale efforts and continue to perform at a high level without increasing headcount. He shared a great example of how a service organization could automate tasks (especially routine or mundane ones) and free reps up to interact with more clients.

Brooks also credits Machine Learning as leading to better and quicker decision making. This type of optimized forecasting, which is easy to quantify, gives you the edge over competitors.

Machine Learning: When It Makes Sense

In certain situations, Machine Learning is a better solution and can solve business problems. For Dawn, if you are looking for patterns then Machine Learning is the way to go. But Eric cautioned that it is not a magic bullet. Machine Learning doesn’t make things possible – it simply makes things much more practical.

When it comes to use cases, Richard advises big organizations (such as institutions of higher education, consumer goods companies and manufacturers) to look at every activity being done internally and determine what humans should be doing and what should you consider turning over to machine attention and effort.

Learning 101: How to Get Educated

This is the first part of several more looks at what the panelists shared that night – Machine Learning is an extremely broad field. When it comes to learning and education – Eric shared some of the night’s best advice.

Machine Learning is the solution to a problem and there are lots of different problems to solve. Focus on problems you care about. Identify a specific problem you want Machine Learning to solve and then work backwards to determine exactly what you need to learn.

This is solid advice and gives you a concrete way to tinker and apply your knowledge.

I am amazed that all of that info was shared in a 45-minute panel discussion.

I would like to extend a huge thank you to Ken, Richard, Eric, Brooks and Dawn for sharing their time and expertise.

It was great to meet so many curious, like-minded people that evening. I know everyone who attended came away with a better understanding of the business impact of ML and the different strategies needed to be successful.